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Big Data and Real Expectations: What Can Your Business Get Out of It?

Big data. It’s in the news, all over your trade journals, and in nearly every blog post you read. Is big data going to revolutionize the world, or is it just another trend that will come and go, leaving no lasting impact? More pointedly, what can your business really stand to gain from big data analytics and aggregation?

Big data is a truly impactful innovation, and it will continue to evolve and prove its usefulness for virtually every industry on earth. Big data has proven useful for predicting viral outbreaks, tracking trends in world markets, and improving ecommerce’s ability to serve customers with timely deals on merchandise they’re actually interested in. When and how can your business take advantage of big data? Here’s what you should know.

What are Your Specific Goals for Big Data?

Big data is powerful in finding correlations between events, but it’s useless in determining causality. For example, big data shows that an increase in non-commercial space flights tends to coincide with the number of people who earn doctorate degrees in sociology. However, no one actually believes that one has anything to do with the other. Similarly, the rise in purchases of organic foods noted by The New York Times between the years 1998 and 2007 correlated with a spike in the number of children diagnosed with autism, but does that really mean organic food consumption causes autism?

What big data analytics and data aggregation are incredibly useful for is finding consistencies amid large sets of data and using those consistencies to make predictions about simple events. For instance, big data has been incredibly useful for predicting the actual arrival times of airlines, resulting in the elimination of millions of dollars of waste per year. Data about weather conditions, the number of planes circling a given airport for landing, and information from radar is far more accurate in predicting what time a plane will actually land than a pilot’s gusstimated ETA. This allows ground crews to be available when the plane is ready to unload, and not be stuck waiting around wasting time. Each airport using this big data technology reports savings of millions of dollars per year.

Big data is incredibly useful and powerful, if you have a clear understanding of what your goals for big data are. However, it can’t be used to determine cause and effect, so it’s important to understand the limitations of big data analysis outside a clearly defined set of goals, such as reducing production times, identifying areas of waste, or determining what particular customers might be interested in buying.

How Large are Your Data Sets?

The bigger the data the better when it comes to predictive analysis by computer.

Big data analysis is more accurate when it has more information to crunch. Data sets that are too large and cumbersome for humans to make sense of are a cinch for good processing power. An example is how Netflix is using big data to dominate the market for on-demand video streaming. Netflix uses big data analytics to gather enormous volumes of data about who watches what, when, how often, and for how long. This data gives them excellent insight into what other programs will be of interest to viewers and allows them to make suggestions to their viewers to keep them engaged.

In the absence of adequate amounts of data, and when data is too expensive to obtain or isn’t in a form usable by computers, human intuition can actually be more accurate. As in the cases of autism and organic foods, or sociology doctorates and non-commercial space flights, humans can make better sense of certain data sets than computers.

To recap, big data analysis can make sense out of large volumes of data and use it to make accurate predictions. However, it has to be really big data to be useful, and you have to realize big data can’t determine cause and effect. Finally, it’s important to establish clearly defined goals for your big data projects, so that your investments are more than just a hopeful shot in the dark.

To help companies cash in on their investments and achieve their big data goals, Syncsort offers a variety of solutions to help manage, store and analyze enterprise-wide data including mainframe, cloud computing and Hadoop.   Learn about Syncsort’s big data integration solutions for Linux, Windows, Unix, Mainframe and more.

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